Drift Flow Matching

2026-05-16

AI Summary

While existing single-step diffusion models are efficient, they lack the flexibility to balance generation quality and computational cost. To address this limitation, this work proposes the Drift Flow Matching framework, which unifies single-step drift modeling with multi-step flow matching for the first time. By integrating the efficiency of direct transport maps with the iterative refinement capability of continuous flows, the framework establishes a new generative paradigm that enables on-demand trade-offs between computational expenditure and output fidelity. Experimental results across diverse tasks and datasets demonstrate that the proposed method achieves a flexible and effective balance between efficiency and quality, exhibiting both strong generalizability and high performance.

Abstract

Iterative generative models such as Flow Matching and Diffusion models have demonstrated strong test-time scaling behavior, where additional inference computation can improve generation quality. In contrast, Drift Models offer efficient one-step generation, but their direct generation paradigm limits such flexibility. In this work, we propose Drift Flow Matching (DFM), a framework that connects drifting generative modeling with flow-based iterative generation. DFM preserves the efficiency of direct transport maps while enabling generation to be refined through multiple inference steps when desired. This bridges the gap between one-step Drift Models and multi-step Flow Matching methods, and provides a novel generative paradigm that can adapt sampling computation to different quality--efficiency requirements. Extensive experiments across different tasks and datasets demonstrate the effectiveness and generality of the proposed framework.

Problem

• Drift Models
• Flow Matching
• Iterative Generation
• One-step Generation
• Generative Modeling

Innovation

• Drift Flow Matching
• iterative generation
• flow-based models
• one-step generation
• test-time scaling